add pymannkendall to dependencies, formatting fixes

environment.yml

@@ -4,6 +4,7 @@ channels:
 dependencies:
   - python >=3.9,<3.12
   - numpy
+  - pymannkendall
   - scipy
   - statsmodels
   - xarray

setup.py

@@ -7,7 +7,15 @@
 with open("README.rst") as readme_file:
     readme = readme_file.read()
 
-requirements = ["numpy", "scipy", "statsmodels", "xarray", "xclim>=0.45.0", "xscen"]
+requirements = [
+    "numpy",
+    "pymannkendall",
+    "scipy",
+    "statsmodels",
+    "xarray",
+    "xclim>=0.45.0",
+    "xscen",
+]
 
 dev_requirements = ["pytest", "pytest-cov"]
 

xhydro/indicators.py

@@ -1,11 +1,12 @@
 """Module to compute indicators using xclim's build_indicator_module_from_yaml."""
 import warnings
+from typing import List, Optional
 
+import numpy as np
+import pymannkendall
 import xarray as xr
 import xclim as xc
 import xscen as xs
-import pymannkendall
-import numpy as np
 from xclim.core.units import rate2amount
 
 # Special imports from xscen
@@ -242,19 +243,39 @@ def get_yearly_op(
 
     return out
 
-def _pval_mannkendall_ufunc(da, 
-                            test, 
-                            outputs):
+
+def _pval_mannkendall_ufunc(da, test, outputs):
     da = da[~np.isnan(da)]
     pmk = getattr(pymannkendall, test)
     mk = pmk(da)
     return tuple([getattr(mk, out) for out in outputs])
 
-def mannkendall(da,
-                     outputs: list = ['h', 'p'], 
-                     test: str ='original_test'):
-    try:
-        return xr.concat(xr.apply_ufunc(_pval_mannkendall_ufunc, da, test, outputs, input_core_dims=[['time'],[], ['dim0']],output_core_dims=np.empty((len(outputs), 0)).tolist(), vectorize=True) , dim='mk').assign_coords(mk=outputs)
-    except:
-        return xr.apply_ufunc(_pval_mannkendall_ufunc, da, test, outputs, input_core_dims=[['time'],[], ['dim0']],output_core_dims=np.empty((len(outputs), 0)).tolist(), output_dtypes=[tuple],vectorize=True).assign_coords(mk=outputs)
 
+def mannkendall(da, outputs: Optional[list[str]] = None, test: str = "original_test"):
+    if outputs is None:
+        outputs = ["h", "p"]
+    try:
+        return xr.concat(
+            xr.apply_ufunc(
+                _pval_mannkendall_ufunc,
+                da,
+                test,
+                outputs,
+                input_core_dims=[["time"], [], ["dim0"]],
+                output_core_dims=np.empty((len(outputs), 0)).tolist(),
+                vectorize=True,
+            ),
+            dim="mk",
+        ).assign_coords(mk=outputs)
+    # FIXME: Are these the only errors that can be raised?
+    except (ValueError, TypeError):
+        return xr.apply_ufunc(
+            _pval_mannkendall_ufunc,
+            da,
+            test,
+            outputs,
+            input_core_dims=[["time"], [], ["dim0"]],
+            output_core_dims=np.empty((len(outputs), 0)).tolist(),
+            output_dtypes=[tuple],
+            vectorize=True,
+        ).assign_coords(mk=outputs)